arXiv subjects

Michael Short

Publications and source records attributed to Michael Short.

11 recordsLinked to original sources

arXiv2026

Distributed Energy System Design including Unbalanced AC Power Flow for Large LV Networks with ADMM

With the addition of large numbers of distributed energy resources (DERs) to distribution networks comes the increasing risk that their operation may violate the safety constraints of these networks. The problem considered in this paper is that of combined siting, sizing and dispatch of these DERs, also known as distributed energy system (DES) design, to help meet electrical and heat loads within the network. Here, the operation of these DERs is modelled, along with the unbalanced three-phase alternating current (AC) power flow in the network. When this network power flow is considered, this admits a non-convex mixed-integer nonlinear program (MINLP) model formulation which scales poorly with network size in terms of solve time. To address this, the problem is decomposed into a series of algorithmic steps, starting with a mixed-integer linear program (MILP) formulation that does not consider network constraints, then fixing binary variables, adding power flow constraints and solving as a nonlinear program (NLP) and finally removing operational binary variables and replacing them with a complementarity reformulation. As the main contributors to the overall solve time, the NLP and Complementarity steps are solved using a hybrid spatial/temporal decomposition strategy and the alternating direction method of multipliers (ADMM) distributed optimisation method. Results are presented for networks based on the European low voltage test feeder with up to 55 loads and 120 timepoints, with the ADMM approach showing speed-ups of up to 13x when considering parallel computation of the subproblems, for a maximum observed optimality gap of 0.61%.

math.OC↗

arXiv2025

A trust-region funnel algorithm for gray-box optimization

Gray-box optimization, where parts of optimization problems are represented by algebraic models while others are treated as black-box models lacking analytic derivatives, remains a challenge. Trust-region (TR) methods provide a robust framework for gray-box problems through local reduced models (RMs) for black-box components, but they are complex and require extensive parameter tuning. Motivated by recent advances in funnel-based convergence theory for nonlinear optimization, we propose a novel TR funnel algorithm for gray-box optimization, replacing the filter acceptance criterion with a uni-dimensional funnel, maintaining a monotonically decreasing upper bound on approximation error of local black-box RMs. A global convergence proof to a first-order critical point is established. The algorithm, implemented open-source in Pyomo, supports multiple RM forms and globalization strategies (filter or funnel). Benchmark tests show the TR funnel algorithm achieves comparable and often improved performance relative to the classical TR filter method, thus providing a simpler, effective alternative for gray-box optimization.

math.OC↗

arXiv2025

Kinetic modelling of the CO2 capture and utilisation on NiRu-Ca/Al dual function material via parameter estimation

This study presents a detailed, open-source kinetic modelling computational framework for CO2 capture and utilisation using a newly formulated dual-function material (DFM) comprising 15 wt% Ni, 1 wt% Ru, and 10 wt% CaO supported on spherical alumina. A finite difference reactor model was developed to simulate the cyclic adsorption, purge, and hydrogenation stages. The model incorporates experimentally-derived rate expressions, accounts for system delay via a second-order response function, and was fitted to time-resolved concentration laboratory data using Bayesian optimisation. A combined parameter estimation strategy was employed to ensure mass continuity across stages and improve the robustness of purge kinetics. The kinetic parameters extracted reveal that carbonate decomposition, not methanation, is the rate-limiting step during hydrogenation. Temperature-dependent simulations confirm a trade-off between reaction kinetics and CO2 storage capacity, with methane yield maximised at 300C when compared with the other temperature sets. By offering transparent methodology and reproducible code, this work provides a robust platform for researchers and practitioners to study, validate, and optimise DFM systems.

physics.chem-ph↗

arXiv2025

Trust-region filter algorithms utilizing Hessian information for gray-box optimization

Optimizing industrial processes often involves gray-box models that couple algebraic glass-box equations with black-box components lacking analytic derivatives. Such systems challenge derivative-based solvers. The classical trust-region filter (TRF) algorithm provides a robust framework but requires extensive parameter tuning and numerous black-box evaluations. This work introduces four Hessian-informed TRF variants that use projected positive definite Hessians for automatic step scaling and minimal tuning, combined with both low-fidelity (linear, quadratic) and high-fidelity (Taylor series, Gaussian process) surrogates for local black-box approximation. Tested on 25 gray-box benchmarks and five engineering case studies, the new variants achieved up to order-of-magnitude reductions in iterations and black-box evaluations, with reduced sensitivity to tuning parameters relative to the classical TRF algorithm. High-fidelity surrogates solved 92%-100% of problems, compared with 72%-84% for low-fidelity surrogates. The developed TRF methods also outperformed classical derivative-free optimization solvers. Results show that new variants offer robust, scalable alternatives for gray-box optimization.

math.OC↗

arXiv2022

DECO2 An Open-source Energy System Decarbonisation Planning Software Including Negative Emissions Technologies

The deployment of CO2 capture and storage (CCS) and negative emissions technologies (NETs) are crucial to meet the net-zero target by year 2050, as emphasised by the Glasgow Climate Pact. Over the years, several energy planning models have been developed to address the temporal aspects of carbon management. However, limited works have incorporated CCS and NETs for bottom-up energy planning at the individual plant scale, which is considered in this work. The novel formulation is implemented in an open-source energy system software that has been developed in this work for optimal decarbonisation planning. The DECarbonation Options Optimisation (DECO2) software considers multiperiod energy planning with a superstructural model and was developed in Python with an integrated user interface in Microsoft Excel. The software application is demonstrated with two scenarios that differ in terms of the availabilities of mitigation technologies. Results demonstrated the potential of fuel substitutions for low-carbon alternatives in existing coal and natural gas power plants. Additionally, once NETs are mature and are available for commercial deployment, their deployment is crucial in aiding CO2 removal in minimal investment costs scenarios. Overall, the newly developed open-source software demonstrates the importance of determining the optimal deployment of mitigation technologies in meeting climate change targets for each period.

eess.SY↗

arXiv2022

Discrete Optimal Designs for Distributed Energy Systems with Nonconvex Multiphase Optimal Power Flow

The optimal selection, sizing, and location of small-scale technologies within a grid-connected distributed energy system (DES) can contribute to reducing carbon emissions, consumer costs, and network imbalances. This is the first study to present an optimisation framework for obtaining discrete technology sizing and selection for grid-connected DES design, while simultaneously considering multiphase optimal power flow (MOPF) constraints to accurately represent unbalanced low-voltage distribution networks. An algorithm is developed to solve the resulting Mixed-Integer Nonlinear Programming (MINLP) formulation. It employs a decomposition based on Mixed-Integer Linear Programming (MILP) and Nonlinear Programming (NLP), and utilises integer cuts and complementarity reformulations to obtain discrete designs that are also feasible with respect to the network constraints. A heuristic modification to the original algorithm is also proposed to improve computational speed. Improved formulations for selecting feasible combinations of air source heat pumps (ASHPs) and hot water storage tanks are also presented. The algorithms outperform the existing state-of-the-art commercial MINLP solver, which fails to find any solutions in two instances. While feasible solutions were obtained for all cases, convergence was not achieved for all, especially for those involving the larger network. Where converged, the algorithm with the heuristic modification has achieved results up to 70% faster than the original algorithm. Results for case studies suggest that including ASHPs can support up to 16% higher renewable generation capacity compared to gas boilers, albeit with higher ASHP investment costs. The optimisation framework and results can be used to inform stakeholders such as policy-makers and network operators, to increase renewable energy capacity and aid the decarbonisation of domestic heating systems.

math.OC↗

arXiv2022

Complementarity Reformulations for the Optimal Design of Distributed Energy Systems with Multiphase Optimal Power Flow

The design of grid-connected distributed energy systems (DES) has been investigated extensively as an optimisation problem in the past, but most studies do not include nonlinear constraints associated with unbalanced alternating current (AC) power flow in distribution networks. Previous studies that do consider AC power flow use either less complex balanced formulations applicable to transmission networks, or iterative linearisations derived from local power flow solutions and prior knowledge of the design. To address these limitations, this study proposes a new algorithm for obtaining DES design decisions subject to nonlinear power flow models. The use of regularised complementarity reformulations for operational constraints that contain binary variables is proposed. This allows the use of large-scale nonlinear solvers that can find locally optimal solutions, eliminating the need for linearisations and prior knowledge while improving accuracy. DES design models with either multiphase optimal power flow (MOPF), which captures inherent phase imbalances present in distribution networks, or balanced optimal power flow formulations (OPF) are tested using a modified version of the unbalanced IEEE EU low-voltage network. Results are compared with a popular linear DES design framework, which proposes an infeasible operational schedule when tested with MOPF, while the fixed design alone produces the highest annualised costs. Despite the increased complexity, DES with MOPF obtains the best solution, enabling a greater integration of solar capacity and reducing total annualised cost when compared to DES with OPF. The new algorithm achieves a 19% improvement when compared with solving a bi-level model for DES with OPF, where the entire binary topology in the nonlinear model is fixed. The study therefore enables the acquisition of DES designs that can work symbiotically within distribution networks.

math.OC↗

arXiv2021

Optimal Design of Distributed Energy Systems Considering the Impacts on Electrical Power Networks

Distributed energy systems (DES) have the potential to minimise costly network upgrades while increasing the proportion of renewable energy generation in the electrical grid, when properly designed. In contrast, poorly designed DES can accelerate the degeneration of existing network infrastructure. Most optimisation models used to design grid-connected DES have oversimplified or excluded constraints associated with alternating current (AC) power flow, as the latter has been studied in a standalone class of models known as Optimal Power Flow (OPF). A small subset of models, labelled DES-OPF models, have attempted to combine these independent frameworks. However, the impacts of using a DES-OPF framework on the resulting designs, as opposed to a conventional DES framework without AC power flow, have not been studied in previous work. This study aims to shed light on these impacts by proposing a bi-level method to solve the computationally-expensive DES-OPF framework, and simultaneously comparing results to a baseline MILP model that utilises direct current (DC) approximations in place of AC OPF, as found abundantly in literature. Two test cases of varying scale are employed to test the frameworks and compare resulting designs. The practical feasibility of the designs is assessed, based on whether the designs can mitigate network violations and energy wastage during standard operation. Results demonstrate that the baseline MILP underestimates total costs due to its inability to detect current and voltage violations, resulting in a 37% increase for one case study when tested with the DES-OPF framework. Major implications on battery capacity are also observed, primarily to manage higher levels of renewable energy curtailment, which emphasise the need to use DES-OPF frameworks when designing grid-connected DES.

math.OC↗

arXiv2021

A semi-continuous model for transmission of SARS-CoV-2 and other respiratory viruses in enclosed spaces via multiple pathways to assess risk of infection and mitigation strategies

The Covid-19 pandemic has taken millions of lives, demonstrating the tragedy and disruption of respiratory diseases, and how difficult they can be to manage. However, there is still significant debate in the scientific community as to which transmission pathways are most significant and how settings and behaviour affect risk of infection, which all have implications for which mitigation strategies are most effective. This study presents a general model to estimate the rate of viral transfer between individuals, objects, and the air. The risk of infection to individuals in a setting is then computed considering the behaviour and interactions of individuals between themselves and the environment in the setting, survival times of the virus on different surface types and in the air, and mitigating interventions (ventilation, hand disinfection, surface cleaning, etc.). The model includes discrete events such as touch events, individuals entering/leaving the setting, and cleaning events. We demonstrate the model capabilities on three case studies to quantify and understand the relative risk associated with the different transmission pathways and the effectiveness of mitigation strategies in different settings. The results show the importance of considering all transmission pathways and their interactions, with each scenario displaying different dominant pathways depending on the setting and behaviours of individuals therein. The flexible model, which is freely available, can be used to quickly simulate the spread of any respiratory virus via the modelled transmission pathways and the efficacy of potential mitigation strategies in any enclosed setting by making reasonable assumptions regarding the behaviour of its occupants. It is hoped that the model can be used to inform sensible decision-making regarding viral infection mitigations that are targeted to specific settings and pathogens.

math.DS↗

arXiv2020

Balancing Accuracy and Complexity in Optimisation Models of Distributed Energy Systems and Microgrids with Optimal Power Flow: A Review

Optimisation and simulation models for the design and operation of grid-connected distributed energy systems (DES) often exclude the inherent nonlinearities related to power flow and generation and storage units, to maintain an accuracy-complexity balance. Such models may provide sub-optimal or even infeasible designs and dispatch schedules. In DES, optimal power flow (OPF) is often misrepresented and treated as a standalone problem. OPF consists of highly nonlinear and nonconvex constraints related to the underlying alternating current (AC) distribution network. This aspect of the optimisation problem has often been overlooked by researchers in the process systems and optimisation area. In this review we address the disparity between OPF and DES models, highlighting the importance of including elements of OPF in DES design and operational models to ensure that the design and operation of microgrids meet the requirements of the electrical grid. By analysing foundational models for both DES and OPF, we identify detailed technical power flow constraints that have been typically represented using oversimplified linear approximations in DES models. We also identify a subset of models, labelled DES-OPF, which include these detailed constraints and use innovative optimisation approaches to solve them. Results of these studies suggest that achieving feasible solutions with high-fidelity models is more important than achieving globally optimal solutions using less-detailed DES models. Recommendations for future work include the need for more comparisons between high-fidelity models and models with linear approximations, and the use of simulation tools to validate DES-OPF models. The review is aimed at a multidisciplinary audience of researchers and stakeholders who are interested in modelling DES to support the development of more robust and accurate optimisation models for the future.

math.OC↗

arXiv2020

Go local: The key to controlling the COVID-19 pandemic in the post lockdown era

The UK government announced its first wave of lockdown easing on 10 May 2020, two months after the non-pharmaceutical measures to reduce the spread of COVID-19 were first introduced on 23 March 2020. Analysis of reported case rate data from Public Health England and aggregated and anonymised crowd level mobility data shows variability across local authorities in the UK. A locality-based approach to lockdown easing is needed, enabling local public health and associated health and social care services to rapidly respond to emerging hotspots of infection. National level data will hide an increasing heterogeneity of COVID-19 infections and mobility, and new ways of real-time data presentation to the public are required. Data sources (including mobile) allow for faster visualisation than more traditional data sources, and are part of a wider trend towards near real-time analysis of outbreaks needed for timely, targeted local public health interventions. Real time data visualisation may give early warnings of unusual levels of activity which warrant further investigation by local public health authorities.

cs.CY↗